1. www.intel.com/labs AAWG Meeting Elements of a Vision Milan Milenkovic, Director, Distributed Systems Architecture CTG, Intel Corporation 10/15/2002

2. www.intel.com/labs 2 Vision: Modular, Internet-scale resource and service pool  Virtualization, dis-aggregation  components of monolithic computer systems - such as storage, cycles, and UI – are virtualized as (web) services that can be accessed and invoked via private or public networks using Internet protocols  result: a vast pool of resources and services is available on public and private networks for completing user tasks (some free, some for fee)  Aggregation  when a task (application) needs to be completed, the necessary resources are obtained from the pool  discovered, selected – based on criteria such as proximity, performance, ownership, cost - and aggregated to meet the needs of the task at hand  Reuse  when the task is completed, resources are returned to the pool

3. www.intel.com/labs 3 Scalability and Usage Examples  Personal (proximity) Area Network  user carries some of her hardware and data, e.g. IR personal server, augments its capabilities – such as storage and UI – from the environment  NB: assumes pervasive, rich embedded computer resources in the environment, initially hot spots “smart spaces”  Home Area Network  home PCs pool their collective cycles temporarily to compress edited home movie (e.g. into DVD-R format)  NB: users benefit from having more, higher-end PCs  intranet/Internet  a company pools its workstations, servers and (off hours) office PCs into a grid to perform crash testing  NB: motivated to procure high-end office PCs

4. www.intel.com/labs 4 Customer View, Benefits  Ultimate Mobility  Seamless, untethered, access to computing resources embedded in the environment (cycles, storage, UI, connectivity)  Sharing of Resources  Better return on investment; resource pooling, aggregation, and load balancing; reliability; automation for business and consumer  Behavioral attributes  Adaptive to changes in the environment  bandwidth variation, intermittent connectivity and node availability  Responsive to user needs  content associated with user, not devices; power on demand

5. www.intel.com/labs 5 (Enabling) Model Behind the Vision  Internet Distributed Computing  Dynamic, task-driven configuration of computing elements  A universe of globally connected and composable services, resources (e.g. cycles, storage), devices  Internet as a computing platform  Ability to discover and dynamically combine components into task-specific functional groupings  aggregate, augment device capability  capacity on demand, reusable: return to pool when task completed  Multiple scales: proximity-area to global grids

6. www.intel.com/labs 6 Vision: Design Implications  Edge processing (rich clients, edge servers)  near target rather than all the way back to centralized servers (scale: supports more devices, connectivity: saves upstream bandwidth)  NB: Edge is essentially any device embedded in the network that can render the service, e.g. a PC; this is NOT ISP-server edge model  Ad-hoc networking (greater device “intelligence”, autonomy)  discovery, self-configuring: for scale and mobility  Data-centric model (user-centered, not device-centered data)  access same data from a variety of devices  Intermittent connectivity (local storage, caching)  devices move in and out of range, power down  caching, data and service proxies

7. www.intel.com/labs 7 Fundamental Components/Principles  To realize the vision, scale from PAN to global grid, need:  virtualization of resources: cycles, storage, UI  resource discovery, dynamic configuration  ad-hoc networking, run-time binding, platform independence  aggregation and orchestration of resources  pooling, dispatching, synchronization  security, authentication  by ownership, proximity, organizational boundaries, intranet, Internet  DSL doing research and concept proofs of IDC fundamentals

8. www.intel.com/labs 8 Industry Trends, Drivers  Demands for efficient use of computing resources  Utility computing  resource pooling and load balancing, dynamic allocation/provisioning of servers, storage  Autonomic computing  automated operation, self-management  Grid computing  share and amplify compute power, access to unique data  Real-world awareness, interaction  sensors, automation

9. www.intel.com/labs 9 Requirements, Building Blocks for Future Environments  Pervasive/ubiquitous computing  scale++: Internet of things (devices, sensors, objects)  compute resources all around in the environment  initially islands of “smart spaces”  utility access gateways and servers – cache and stage data, conserve power on portable devices  users carry some of their compute/data, e.g. personal server, augment capabilities from the environment  Proactive computing  react to real-world stimuli, initiate actions based on user’s context (intent, activity, location)

10. www.intel.com/labs 10 Assumptions and Design Implications  Connectivity – hybrid: wireless, wired (proxy)  wired connection (WAN), combined with short-range, high-bandwidth (Mbps+) wireless  aggregate bandwidth on edge >> backbone  Scale – number of devices  edge processing: process and filter near source vs. communication to remote, centralized servers  ad-hoc networking (mobility/wireless networks, too many devices to configure and keep up to date)  Users carry some of their compute/data, e.g. personal server, augment capabilities from the environment

11. www.intel.com/labs Backup

12. www.intel.com/labs 12 Goal  Accelerate distributed computing on the Internet, increases end-user value (Biz and Consumer), Silicon use  Define and Extol IDC stack.  Enhanced Web Service Technology  Integrated Managed Run Time Environments  Provide a foundation for pervasive computing from a small-scale PAN to virtual, planetary grids.

13. www.intel.com/labs 13 IDC Benefits for the World  Dynamic aggregation of resources for computing on demand.  Reusable SW and associated developer benefit.  Foundation for proactive and pervasive computing.  Reduce manageability cost and complexity.  More efficient use of computing resources.  Reliability, availability and scalability.

14. www.intel.com/labs 14 Converged Middleware: Grid, P2P, and WbS: “Internet Distributed Computing” Web Service Protocols (XML, SOAP, WSDL, UDDI, reliable messaging) Separate Communities, Now Converging Web Services Grid Grid Peer to Peer Virtualized Resources (computation, storage, apps) Resource description and discovery eBusiness (Workflow) Grid (HPC) P2P (Collaboration) Multi-Scale, Multi-purpose Dynamic, Task-Specific Aggregation of Resources IDC Apps

15. www.intel.com/labs 15 Converging Functional Stacks Standards Management & Policies Communication Platform, OS, Local Resources Naming, Virtual Resource Management (Run-time) Binding Publishing, Discovery, Description Composition, Orchestration (FC) Applications Security Reliability, Availability, Serviceability Platform, OS, Local Resources WbS P2P, grid

16. www.intel.com/labs 16 Implications/Requirements  data-centric model  access same data from a variety of devices  intermittent connectivity  devices move in and out of range, power down  caching, data and service proxies